جستجوی مقالات مرتبط با کلیدواژه « روش پارامتری » در نشریات گروه « علوم دام »

This study aimed to investigate the  effect  of  the method of estimating the effects of markers , QTLs distribution, number of QTLs, effective population size and trait heritability on the accuracy of genomic predictions. Two effective population sizes, 100 and 500 individuals, were simulated by QMSim software. A 100 cM genome including one chromosome was simulated where 500 SNPs and two different  numbers of QTLs (50 and 200) were distributed on it randomly. In this study three levels of heritability (0.1, 0.3 and 0.5) were considered. Genomic breeding values were predicted using Bayesian ridge regression, BayesA, BayesB, BayesC, Bayesian LASSO, Reproducing kernel Hilbert space and neural networks. In this research, the accuracy of genomic breeding values were affected by trait heritability, effective population size, markers effect estimation methods, QTLs distribution and number of QTLs. The Bayes A and B had the highest accuracy while accuracy of neural networks method was the lowest. The accuracy of genomic breeding values were increased as the heritability of trait and number of QTLs increased while the accuracy was decreased as the effective population decreased. Considering the QTLs distribution, the highest accuracy was achieved when the QTLs distributed normally.

Accurate estimation of variance components using pedigree and genomic data plays a key role in prediction of breeding values. Since SNP markers in genomic selection are distributed across the genome, they may cover all quantitative traits loci and potentially explain all of genetic variation. In this study, genotype data from Merino sheep, genotyped by 50k Illumina SNP chip were used. Staple length and Fibre diameter traits were studied in this research. To study the association between allele frequency spectrum and captured additive genetic variance, all SNPs were partitioned in five MAF bins with the equal numbers of SNPs. Two statistical models including separate analysis for each category of MAF SNPs or joint analysis of all MAF groups were fitted. The analysis were performed using REML (parametric) and a Bayesian method implemented via Gibbs sampling and RKHS (semi-parametric) model. Using all common SNPs in REML approach, estimates of genomic heritability were 0.72 and 0.48 for Staple length and Fibre diameter, respectively. In Bayesian approach, genomic heritability for mentioned traits were 0.74 and 0.47 respectively. In the separate analysis, estimates of genomic heritability using REML and Bayesian approaches for each MAF class were similar, but in joint analysis estimates of two approaches were different. Overall, when the model is simple both approaches perform similarly while when model is complicated as joint analysis in present study, two approaches work different. Therefore, to determine which approach is more reliable, further research is required